def LORETA(alpha, K, bkdir, MaxNeighbor):
Ne, Nd = K.shape
H = np.eye((Ne))- np.ones((Ne, Ne))/Ne
K = np.dot(H, K)
for bkdfilename1 in glob.iglob(bkdir+"/*.Dipoles_1_*"):
D1 = read_bk.bkd(bkdfilename1)
for bkdfilename2 in glob.iglob(bkdir+"/*.Dipoles_2_*"):
D2 = read_bk.bkd(bkdfilename2)
Nd = D1['ndipoles'] + D2['ndipoles']
Mat1 = np.concatenate((np.matrix(D1['dipole_location_index']['x']),np.matrix(D1['dipole_location_index']['y']), np.matrix(D1['dipole_location_index']['z'])), axis=0)
Mat2 = np.concatenate((np.matrix(D2['dipole_location_index']['x']),np.matrix(D2['dipole_location_index']['y']), np.matrix(D2['dipole_location_index']['z'])), axis=0)
VoxCoord = np.array(np.concatenate((Mat1, Mat2), axis=1))
Vox2 = VoxCoord*VoxCoord
#vcnorms = np.matrix(np.sum(VoxCoord*VoxCoord, axis=0))
#vcnorms = np.matrix(Vox2.sum(axis=0))
vcnorms = np.matrix(np.sum(Vox2, axis=0))
tridist = vcnorms - 2 * np.dot( VoxCoord.T, VoxCoord) + vcnorms.T
tridist= np.sqrt(tridist)
Neighbor = np.array(tridist < MaxNeighbor, dtype=float)
B = np.identity(Nd)
for i in range(Nd):
Ni = np.sum(Neighbor[i,:])-1
if Ni > 0:
B[i,:] = -Neighbor[i,:]/Ni
B[i,i] = 1
BigK = np.concatenate((K, alpha * B), axis=0)
KtK = np.dot(BigK.T, BigK)
T = np.dot(np.linalg.inv(KtK), BigK.T)
Imatrix = T[:, :Ne]
return Imatrix
from RabbitMQ import Connection
i = 83
DipI = np.zeros(nD)
DipI[i] = 1
plt.plot(K[:, i])
plt.show()
c = Connection.Connection('localhost')
c.connect()
c.sendOrientedData(DipI, "Ori", "4:00pm")
c.sendEEGData(K[:, i], "EEG", "")
c.disconnect()
Dip1 = read_bk.bkd(
'/Users/jesong1126/Work/EAV_MIE_BrainK/r842_ihm_test1/r842_ihm_test1.Dipoles_1_MRI.bkd'
)
Dip2 = read_bk.bkd(
'/Users/jesong1126/Work/EAV_MIE_BrainK/r842_ihm_test1/r842_ihm_test1.Dipoles_2_MRI.bkd'
)
d1 = Dip1['dipole_location_index']
d2 = Dip2['dipole_location_index']
Dip_x = np.concatenate((d1['x'], d2['x']), axis=0)
Dip_y = np.concatenate((d1['y'], d2['y']), axis=0)
Dip_z = np.concatenate((d1['z'], d2['z']), axis=0)
Dip_xyz = np.concatenate((Dip_x, Dip_y, Dip_z), axis=0)
Dip_xyz = Dip_xyz.reshape(3, nD)
Dip_xyz = Dip_xyz.T
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 30 10:45:29 2015
@author: jesong1126
"""
#import os
#os.chdir("/Users/jesong1126/Work/Data/EAV/2941_HM")
import numpy as np
import matplotlib.pyplot as plt
##
from braink import read_bk
lbkd = read_bk.bkd('/Users/jesong1126/Python27/GeoPy/data/108_MIE/108_3.Dipoles_left_MRI.bkd')
rbkd = read_bk.bkd('/Users/jesong1126/Python27/GeoPy/data/108_MIE/108_3.Dipoles_right_MRI.bkd')
lbkd['ndipoles'] + rbkd['ndipoles']
lcoord = np.array((lbkd['dipole_location_index']['x'], lbkd['dipole_location_index']['y'], lbkd['dipole_location_index']['z'])).T
rcoord = np.array((rbkd['dipole_location_index']['x'], rbkd['dipole_location_index']['y'], rbkd['dipole_location_index']['z'])).T
VoxCoord = np.concatenate((lcoord, rcoord), axis=0)
nVL = lbkd['ndipoles']
nVR = rbkd['ndipoles']
mmin = np.min(VoxCoord, axis=0)
mmax = np.max(VoxCoord, axis=0)
fig, ax = plt.subplots(2,2)
ax[0,0].scatter(VoxCoord[:,0], VoxCoord[:,1], s=100, cmap='hot') #c=nSigVoxGrpI,
ax[0,0].set_axis_off()
ax[0,0].set_aspect('equal')
ax[0,0].set_xlim([mmin[0]-10, mmax[0]+10])
ax[0,0].set_ylim([mmin[1]-10, mmax[1]+10])
import os
os.system("open /Applications/EAV/Mimir.app")
from RabbitMQ import Connection
i=83; DipI= np.zeros(nD); DipI[i] = 1
plt.plot(K[:,i]); plt.show()
c = Connection.Connection('localhost')
c.connect()
c.sendOrientedData(DipI, "Ori", "4:00pm")
c.sendEEGData(K[:, i], "EEG", "")
c.disconnect()
Dip1 = read_bk.bkd('/Users/jesong1126/Work/EAV_MIE_BrainK/r842_ihm_test1/r842_ihm_test1.Dipoles_1_MRI.bkd')
Dip2 = read_bk.bkd('/Users/jesong1126/Work/EAV_MIE_BrainK/r842_ihm_test1/r842_ihm_test1.Dipoles_2_MRI.bkd')
d1 = Dip1['dipole_location_index']; d2 = Dip2['dipole_location_index'];
Dip_x = np.concatenate((d1['x'], d2['x']), axis=0)
Dip_y = np.concatenate((d1['y'], d2['y']), axis=0)
Dip_z = np.concatenate((d1['z'], d2['z']), axis=0)
Dip_xyz = np.concatenate((Dip_x, Dip_y, Dip_z), axis=0)
Dip_xyz = Dip_xyz.reshape(3,nD)
Dip_xyz = Dip_xyz.T
Dip_xyz[i,:]
print(np.min(d1['x']), np.max(d1['x']), np.min(d2['x']), np.max(d2['x']))
